Biotechnologies for cancer diagnostics : cell sorting, protein analysis and imaging of cellular metabolism

This thesis presents the development of chip-based technology for informative in vitro cancer diagnostics. In the first part of this thesis, I will present my contribution in the development of a technology called “Nucleic Acid Cell Sorting (NACS)”, based on microarrays composed of nucleic acid encoded peptide major histocompatibility complexes (p/MHC), and the experimental and theoretical methods to detect and analyze secreted proteins from single or few cells.

Secondly, a novel portable platform for imaging of cellular metabolism with radio probes is presented. A microfluidic chip, so called “Radiopharmaceutical Imaging Chip” (RIMChip), combined with a beta-particle imaging camera, is developed to visualize the uptake of radio probes in a small number of cells. Due to its sophisticated design, RIMChip allows robust and user-friendly execution of sensitive and quantitative radio assays. The performance of this platform is validated with adherent and suspension cancer cell lines. This platform is then applied to study the metabolic response of cancer cells under the treatment of drugs. Both cases of mouse lymphoma and human glioblastoma cell lines, the metabolic responses to the drug exposures are observed within a short time (~ 1 hour), and are correlated with the arrest of cell-cycle, or with changes in receptor tyrosine kinase signaling.

The last parts of this thesis present summaries of ongoing projects: development of a new agent as an in vivo imaging probe for c-MET, and quantitative monitoring of glycolytic metabolism of primary glioblastoma cells. To develop a new agent for c-MET imaging, the one-bead-one-compound combinatorial library method is used, coupled with iterative screening. The performance of the agent is quantitatively validated with cell-based fluorescent assays. In the case of monitoring the metabolism of primary glioblastoma cell, by RIMChip, cells were sorting according to their expression levels of oncoprotein, or were treated with different kinds of drugs to study the metabolic heterogeneity of cancer cells or metabolic response of glioblastoma cells to drug treatments, respectively.

Analysis-aware design of embedded systems software

In the past many different methodologies have been devised to support software development and different sets of methodologies have been developed to support the analysis of software artefacts. We have identified this mismatch as one of the causes of the poor reliability of embedded systems software. The issue with software development styles is that they are ``analysis-agnostic.'' They do not try to structure the code in a way that lends itself to analysis. The analysis is usually applied post-mortem after the software was developed and it requires a large amount of effort. The issue with software analysis methodologies is that they do not exploit available information about the system being analyzed.

In this thesis we address the above issues by developing a new methodology, called "analysis-aware" design, that links software development styles with the capabilities of analysis tools. This methodology forms the basis of a framework for interactive software development. The framework consists of an executable specification language and a set of analysis tools based on static analysis, testing, and model checking. The language enforces an analysis-friendly code structure and offers primitives that allow users to implement their own testers and model checkers directly in the language. We introduce a new approach to static analysis that takes advantage of the capabilities of a rule-based engine. We have applied the analysis-aware methodology to the development of a smart home application.